1. Field of the Invention
This invention relates in general to a relational database management system, and in particular, to data mining assists in a relational database management system.
2. Description of Related Art
Relational databases are the predominate form of database management systems used in computer systems. Relational database management systems are often used in so-called “data warehouse” applications where enormous amounts of data are stored and processed. In recent years, several trends have converged to create a new class of data warehousing applications known as data mining applications. Data mining is the process of identifying and interpreting patterns in databases, and can be generalized into three stages.
Stage one is the reporting stage, which analyzes the data to determine what happened. Generally, most data warehouse implementations start with a focused application in a specific functional area of the business. These applications usually focus on reporting historical snap shots of business information that was previously difficult or impossible to access. Examples include Sales Revenue Reporting, Production Reporting and Inventory Reporting to name a few.
Stage two is the analyzing stage, which analyzes the data to determine why it happened. As stage one end-users gain previously unseen views of their business, they quickly seek to understand why certain events occurred; for example a decline in sales revenue. After discovering a reported decline in sales, data warehouse users will then obviously ask, “Why did sales go down?” Learning the answer to this question typically involves probing the database through an iterative series of ad hoc or multidimensional queries until the root cause of the condition is discovered. Examples include Sales Analysis, Inventory Analysis or Production Analysis.
Stage three is the predicting stage, which tries to determine what will happen. As stage two users become more sophisticated, they begin to extend their analysis to include prediction of unknown events. For example, “Which end-users are likely to buy a particular product”, or “Who is at risk of leaving for the competition?” It is difficult for humans to see or interpret subtle relationships in data, hence as data warehouse users evolve to sophisticated predictive analysis they soon reach the limits of traditional query and reporting tools. Data mining helps end-users break through these limitations by leveraging intelligent software tools to shift some of the analysis burden from the human to the machine, enabling the discovery of relationships that were previously unknown.
Many data mining technologies are available, from single algorithm solutions to complete tool suites. Most of these technologies, however, are used in a desktop environment where little data is captured and maintained. Therefore, most data mining tools are used to analyze small data samples, which were gathered from various sources into proprietary data structures or flat files. On the other hand, organizations are beginning to amass very large databases and end-users are asking more complex questions requiring access to these large databases.
Unfortunately, most data mining technologies cannot be used with large volumes of data. Further, most analytical techniques used in data mining are algorithmic-based rather than data-driven, and as such, there are currently little synergy between data mining and data warehouses. Moreover, from a usability perspective, traditional data mining techniques are too complex for use by database administrators and application programmers.
Thus, there is a need to scale data mining applications to large databases. In addition, there is a need in the art for improved techniques of data extraction from large databases for the purposes of data mining. Moreover, there is a need in the art for improved interfaces between large databases and data mining applications.